Update ingest.py

ingest.py

@@ -1,32 +1,35 @@
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
-from langchain.text_splitter import RecursiveCharacterTextSplitter 
-from langchain.embeddings import SentenceTransformerEmbeddings 
-from langchain.vectorstores import Chroma 
-import os 
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
 from constants import CHROMA_SETTINGS
 
 persist_directory = "db"
 
 def main():
-    for root, dirs, files in os.walk("docs"):
-        for file in files:
-            if file.endswith(".pdf"):
-                print(file)
-                loader = PyPDFLoader(os.path.join(root, file))
-    documents = loader.load()
-    print("splitting into chunks")
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
-    texts = text_splitter.split_documents(documents)
-    #create embeddings here
-    print("Loading sentence transformers model")
-    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-    #create vector store here
-    print(f"Creating embeddings. May take some minutes...")
-    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
-    db.persist()
-    db=None 
-
-    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
+    st.title("PDF Processor")
+
+    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+    if uploaded_file is not None:
+        st.write("Processing PDF...")
+        loader = PyPDFLoader(uploaded_file)
+        documents = loader.load()
+
+        st.write("Splitting into chunks")
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+        texts = text_splitter.split_documents(documents)
+
+        st.write("Loading sentence transformers model")
+        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+        st.write("Creating embeddings. This may take some time...")
+        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+        db.persist()
+        db = None
+
+        st.success("Ingestion complete! You can now run privateGPT.py to query your documents")
 
 if __name__ == "__main__":
-    main()
+    main()